BEGIN:VCALENDAR VERSION:2.0 PRODID:-//UCLA Center for Social Statistics - ECPv6.15.17.1//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-ORIGINAL-URL:https://css.pre2.ss.ucla.edu X-WR-CALDESC:Events for UCLA Center for Social Statistics REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20160313T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20161106T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20170312T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20171105T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20180311T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20181104T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20190310T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20191103T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20200308T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20201101T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20210314T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20211107T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20220313T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20221106T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20230312T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20231105T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20240310T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20241103T090000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20230419T120000 DTEND;TZID=America/Los_Angeles:20230419T133000 DTSTAMP:20260610T204835 CREATED:20250424T061630Z LAST-MODIFIED:20250424T061657Z UID:6435-1681905600-1681911000@css.pre2.ss.ucla.edu SUMMARY:Workshop: Preproducibility: what we may not\, with advantage\, omit DESCRIPTION:Workshop: Preproducibility: what we may not\, with advantage\, omit \nPlease note that there will be no remote attendance for this event. All attendees must attend the workshop in person.  \nPanelists: Philip B. Stark (Remote)\, Yotam Shem-Tov\, Irene Bloemraad (Remote)\, and Randall Kuhn \nModerator: Patrick Heuveline \nPresenter:  \nPhilip B. Stark is Distinguished Professor of Statistics at the University of California\, Berkeley. He holds an AB in Philosophy from Princeton University and a PhD in Earth Sciences from the University of California\, San Diego. His research interests include philosophy of science and foundations of probability and statistics\, active transportation\, cosmology\, elections\, earthquakes\, gender bias\, lottery fraud\, nonparametric statistics\, physics\, regenerative agriculture\, simulation\, uncertainty quantification\, and wild food in urban ecosystems. Methods he invented for auditing elections are in law in about ten states. He has served as an expert witness or consultant for many Fortune 500 companies; the U.S. departments of Agriculture\, Commerce\, Housing and Urban Development\, Justice\, and Veterans Affairs; and numerous state agencies. He currently serves on the Board of Advisors of the U.S. Election Assistance Commission. \nWorkshop: Preproducibility: what we may not\, with advantage\, omit \nWorkshop Description: Karl Popper (1992) wrote: “Science may be described as the art of systematic oversimplification — the art of discerning what we may with advantage omit.” Communicating a scientific result requires enumerating\, recording and reporting those things that cannot with advantage be omitted. At the dawn of the Enlightenment\, chemist Robert Boyle (1660) wrote The New Experiments so “that the person I addressed them to might\, without mistake\, and with as little trouble as possible\, be able to repeat such unusual experiments.” An experiment or analysis is preproducibleif it has been described in adequate detail for others to repeat it. Most current published science is not preproducible. We need to fix that. \nModerator:  \nPatrick Heuveline is a Professor of Sociology at UCLA. He is also the Associate Director of the UCLA California Center for Population Research. \nPanelists: \nYotam Shem-Tov is an Assistant Professor of Economics at UCLA. His research primarily focuses on Labor and Public Economics with a special interest in the U.S. criminal justice system. He received a BA in Economics and Philosophy from Tel-Aviv University and a PhD in Economics from UC Berkeley. \nIrene Bloemraad (Remote) (Ph.D. Harvard; M.A. McGill) is the Class of 1951 Professor of Sociology. She also serves as the Thomas Garden Barnes Chair of Canadian Studies at Berkeley\, is the founding Director of the Berkeley Interdisciplinary Migration Initiative\, and co-directs the Boundaries\, Membership and Belonging program of the Canadian Institute for Advanced Research.In 2014-15\, she was a member of the U.S. National Academies of Sciences committee reporting on the integration of immigrants into American society. \nRandall Kuhn  is a demographer and sociologist focused on the social determinants of health among vulnerable populations. He is an expert in survey design\, longitudinal analysis and counterfactual research design. In the field of migration and health\, Kuhn has designed new approaches to estimating the impact of migration on health. In global health\, Kuhn leads a 35-year longitudinal study of the impact of health and development programs in Bangladesh. In the area of homelessness\, Kuhn conducted some of the earliest quantitative research on health and substance use risks among chronically homeless adults. He co-authored recent reports on homelessness and the coronavirus outbreak for the National Alliance to End Homelessness and on health and homelessness in Los Angeles. URL:https://css.pre2.ss.ucla.edu/event/workshop-preproducibility-what-we-may-not-with-advantage-omit/ LOCATION:4240A Public Affairs Bldg CATEGORIES:CCPR Seminar,CCPR Workshop END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20230201T120000 DTEND;TZID=America/Los_Angeles:20230201T132000 DTSTAMP:20260610T204835 CREATED:20250424T060756Z LAST-MODIFIED:20250424T060756Z UID:6431-1675252800-1675257600@css.pre2.ss.ucla.edu SUMMARY:Graeme Blair\, UCLA DESCRIPTION:Biography: Graeme Blair is an associate professor of political science at UCLA and serves as Co-Director of Training and Methods of Evidence in Governance and Politics (EGAP). Graeme uses experiments\, field research\, and statistics to study how to reduce violence and how to improve social science research. He works primarily in Nigeria\, often in partnership with government\, civil society\, or international organizations. His work is published in journals including Science\, Proceedings of the National Academy of Sciences\, Science Advances\, American Political Science Review\, American Journal of Political Science\, Journal of Politics\, Journal of the American Statistical Association\, and Political Analysis. His book on community policing is forthcoming with Cambridge University Press Studies in Comparative Politics and his book on research design is forthcoming with Princeton University Press. He is the recipient of the Leamer-Rosenthal Prize for Open Social Science\, the Society for Political Methodology best statistical software award\, and the Pi Sigma Alpha best paper award. \nBetter research planning through simulation\nAbstract: The talk introduces a new way of thinking about research designs in the social sciences\, with the aim of making it easier to develop and to share strong research designs. At the heart of our approach is the MIDA framework\, in which a research design is characterized by four elements: a model\, an inquiry\, a data strategy\, and an answer strategy. We have to understand each of the four on their own and also how they interrelate. The design encodes your beliefs about the world\, it describes your questions\, and it lays out how you go about answering those questions\, both in terms of what data you collect and how you analyze it. In strong designs\, choices made in the model and inquiry are reflected in the data and answer strategies\, and vice-versa. This way of thinking pays dividends at multiple points in the research design lifecycle: planning the design\, implementing it\, and integrating the results into the broader research literature. The declaration\, diagnosis\, and redesign process informs choices made from the beginning to the end of a research project. These ideas will appear in Research Design in the Social Sciences: Declaration\, Diagnosis\, and Redesign\, forthcoming in the fall with Princeton University Press. \nTo access the recording please click here. URL:https://css.pre2.ss.ucla.edu/event/graeme-blair-ucla/ LOCATION:4240A Public Affairs Bldg CATEGORIES:CCPR Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20220216T120000 DTEND;TZID=America/Los_Angeles:20220216T133000 DTSTAMP:20260610T204835 CREATED:20250424T063107Z LAST-MODIFIED:20250424T063107Z UID:6457-1645012800-1645018200@css.pre2.ss.ucla.edu SUMMARY:Jack Mountjoy\, University of Chicago DESCRIPTION:Biography: Jack Mountjoy is an Assistant Professor of Economics and Robert H. Topel Faculty Scholar at the University of Chicago Booth School of Business. His research explores the economics and econometrics of education\, labor markets\, and social mobility. Prior to joining Chicago Booth\, he was a Postdoctoral Fellow in Economics at Princeton University in the Industrial Relations Section. He is a Faculty Research Fellow at the National Bureau of Economic Research and a Research Affiliate at the University of Chicago Inclusive Economy Lab\, MIT Blueprint Labs\, and Statistics Norway. \nJack holds a Ph.D. in Economics from the University of Chicago\, where his dissertation work earned a fellowship from the National Academy of Education and Spencer Foundation. He also holds a post-baccalaureate Certificate in Mathematics from George Washington University and a B.A. in Economics and Politics from Whitman College. \n\n\nThe Returns to College(s): Relative Value-Added and Match Effects in Higher Education\n\nAbstract: Students who attend different colleges in the U.S. end up with vastly different economic outcomes. We study the role of relative value-added across colleges within student choice sets in producing these outcome disparities. Linking administrative high school records\, college applications\, admissions decisions\, enrollment spells\, degree completions\, and quarterly earnings spanning the Texas population\, we identify relative college value-added by comparing the outcomes of students who apply to and are admitted by the same set of institutions\, as this approach strikingly balances observable student potential across college treatments and renders our extensive set of covariates irrelevant as controls. Methodologically\, we develop a framework for identifying and interpreting value-added under varying assumptions about match effects and sorting gains\, generalizing the constant treatment effects assumption typically employed in the value-added literature. Empirically\, we estimate a relatively tight\, though non-degenerate\, distribution of relative value-added across the wide diversity of Texas public universities. Selectivity poorly predicts value-added within student choice sets: a fleeting selectivity earnings premium fades to zero after a few years in the labor market\, and more selective colleges tend to have lower value-added on STEM degree completion. Non-peer college inputs like instructional spending more strongly predict value-added\, especially conditional on selectivity. Educational impacts predict labor market impacts: colleges with larger earnings value-added also tend to be colleges that boost persistence\, BA completion\, and STEM degrees along the way. Finally\, we probe the potential for (mis)match effects by allowing each college’s relative value-added to vary flexibly by student characteristics. At first glance\, Black students appear to face small negative returns to choosing more selective colleges\, but this pattern of modest “mismatch” is entirely driven by the availability of two large historically Black universities with low selectivity but above-average value-added. Across the non-HBCUs\, Black students face similar returns to selectivity\, and indistinguishable value-added schedules more generally\, compared to their peers from other backgrounds.\n\nYou can access the CCPR seminar using this link.\nA recording of the seminar may be accessed here. URL:https://css.pre2.ss.ucla.edu/event/jack-mountjoy-university-of-chicago/ LOCATION:Zoom seminar. Please contact ccpradmin@ccpr.ucla.edu for Zoom link. CATEGORIES:CCPR Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20220202T120000 DTEND;TZID=America/Los_Angeles:20220202T133000 DTSTAMP:20260610T204835 CREATED:20250424T062904Z LAST-MODIFIED:20250424T062904Z UID:6454-1643803200-1643808600@css.pre2.ss.ucla.edu SUMMARY:CCPR Workshop: Analyzing Sample Survey Data DESCRIPTION:In this workshop\, attendees will learn how to analyze survey data while accounting for its complex survey design. We will demonstrate how to specify the survey design\, impute any missing data\, and analyze the survey outcomes of interest. We will discuss how our downstream “analysis” steps are related to initial operational “design” choices made by the survey data provider. We will use R but also reference equivalent Stata routines. \nslides \nThe recording may be accessed here. URL:https://css.pre2.ss.ucla.edu/event/ccpr-workshop-analyzing-sample-survey-data/ CATEGORIES:CCPR Seminar,CCPR Workshop END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20211110T120000 DTEND;TZID=America/Los_Angeles:20211110T133000 DTSTAMP:20260610T204835 CREATED:20250424T062723Z LAST-MODIFIED:20250424T062723Z UID:6450-1636545600-1636551000@css.pre2.ss.ucla.edu SUMMARY:Ian Lundberg\, UCLA DESCRIPTION:Prediction in Social Science: A Tool to Study Inequality in Populations \nBiography: Ian Lundberg is a Postdoctoral Scholar in the Department of Sociology and California Center for Population Research at UCLA. His research develops statistical and machine learning methods to answer new questions about inequality in America. Past work is published or forthcoming in PNAS\, the American Sociological Review\, Demography\, the Journal of Policy Analysis and Management\, Sociological Methodology\, Sociological Methods and Research\, and Socius. This academic year\, Ian is working on an NSF-funded postdoctoral project developing computational methods to study income mobility. In 2022\, he will begin as an Assistant Professor in the Department of Information Science at Cornell University. You can read more at ianlundberg.org. \nAbstract: Predictive algorithms could transform methodology in social science\, yet the mapping between prediction and scientific knowledge is not always clear. This talk will address three uses of prediction: (1) predicting outcomes for individual people\, (2) predicting unobserved factual outcomes to describe populations\, and (3) predicting counterfactual outcomes for causal claims. I will argue that prediction of individual-level outcomes is often difficult in social science\, yet predictive algorithms which are imperfect for individuals (1) can nonetheless be useful in support of population-level claims (2 and 3). This framework for the use of prediction is well-suited to the integration of perspectives from social science (defining the population-level quantity to be estimated) and data science (building a predictive model to estimate that quantity). \nYou can access a recording of the presentation here. URL:https://css.pre2.ss.ucla.edu/event/ian-lundberg-ucla/ CATEGORIES:CCPR Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20190508T120000 DTEND;TZID=America/Los_Angeles:20190508T133000 DTSTAMP:20260610T204835 CREATED:20190426T154158Z LAST-MODIFIED:20190426T154158Z UID:6183-1557316800-1557322200@css.pre2.ss.ucla.edu SUMMARY:Brandon Stewart\, Princeton University DESCRIPTION:Title: How to Make Causal Inferences Using Texts \nAbstract: Texts are increasingly used to make causal inferences: either with the document serving as the treatment or the outcome. We introduce a new conceptual framework to understand all text-based causal inferences\, demonstrate fundamental problems that arise when using manual or computational approaches applied to text for causal inference\, and provide solutions to the problems we raise.  We demonstrate that all text-based causal inferences depend upon a latent representation of the text and we provide a framework to learn the latent representation.  Estimating this latent representation\, however\, creates new risks: we may unintentionally create a dependency across observations or create opportunities to fish for large effects.  To address these risks\, we introduce a train/test split framework and apply it to estimate causal effects from an experiment on immigration attitudes and a study on bureaucratic responsiveness.  Our work provides a rigorous foundation for text-based causal inferences\, connecting two previously disparate literatures. (Joint Work with Egami\, Fong\, Grimmer and Roberts) URL:https://css.pre2.ss.ucla.edu/event/brandon-stewart-princeton-university/ LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States\, 101 Sumner Ave\, United States CATEGORIES:CSS Seminar,Divisional Publish END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20190501T120000 DTEND;TZID=America/Los_Angeles:20190501T133000 DTSTAMP:20260610T204835 CREATED:20190426T153944Z LAST-MODIFIED:20190426T153944Z UID:2053-1556712000-1556717400@css.pre2.ss.ucla.edu SUMMARY:Susan Athey\, Stanford University DESCRIPTION:Title: “Estimating Heterogeneous Treatment Effects and Optimal Treatment Assignment Policies” \nAbstract: This talk will review recently developed methods for estimating conditional average treatment effects and optimal treatment assignment policies in experimental and observational studies\, including settings with unconfoundedness or instrumental variables.  Multi-armed bandits for learning treatment assignment policies will also be considered. URL:https://css.pre2.ss.ucla.edu/event/susan-athey-stanford-university/ LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States\, 101 Sumner Ave\, United States CATEGORIES:CSS Seminar,Divisional Publish ATTACH;FMTTYPE=: END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20190312T140000 DTEND;TZID=America/Los_Angeles:20190312T153000 DTSTAMP:20260610T204835 CREATED:20190228T181841Z LAST-MODIFIED:20190228T181841Z UID:1621-1552399200-1552404600@css.pre2.ss.ucla.edu SUMMARY:Eloise Kaizar\, Ohio State University DESCRIPTION:Eloise Kaizar\, Ohio State University \nRandomized controlled trials are often thought to provide definitive evidence on the magnitude of treatment effects. But because treatment modifiers may have a different distribution in a real world population than among trial participants\, trial results may not directly reflect the average treatment effect that would follow real world adoption of a new treatment. Recently\, weight-based methods have been repurposed to more provide more relevant average effect estimates for real populations. In this talk\, I summarize important analytical choices involving what should and should not be borrowed from other applications of weight-based estimators\, make evidence-based recommendations about confidence interval construction\, and present conjectures about best choices for other aspects of statistical inference. \nEloise Kaizar is Associate Professor of Statistics at The Ohio State University. Her primary research focus is on assessing the effects and safety of medical exposures and interventions\, especially those whose effects are heterogeneous across populations or measured with rare event outcomes. As such\, she has worked on methodology to combine multiple sources of information relevant to the same broad policy or patient-centered question. She is particularly interested in how data collected via different study designs can contribute complementary information. URL:https://css.pre2.ss.ucla.edu/event/eloise-kaizar-ohio-state-university/ LOCATION:1434A Physics and Astronomy\, 1434A Physics and Astronomy\, Los Angeles\, CA\, 90098\, United States CATEGORIES:CSS Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20190305T140000 DTEND;TZID=America/Los_Angeles:20190305T153000 DTSTAMP:20260610T204835 CREATED:20190228T181445Z LAST-MODIFIED:20190228T181445Z UID:1617-1551794400-1551799800@css.pre2.ss.ucla.edu SUMMARY:Lan Liu\, University of Minnesota at Twin Cities DESCRIPTION:Lan Liu\, University of Minnesota at Twin Cities\n“Parsimonious Regressions for Repeated Measure Analysis” \nAbstract: Longitudinal data with repeated measures frequently arises in various\ndisciplines. The standard methods typically impose a mean outcome model as\na function of individual features\, time and their interactions. However\,\nthe validity of the estimators relies on the correct specifications of the\ntime dependency. The envelope method is recently proposed as a sufficient\ndimension reduction (SDR) method in multivariate regressions. In this\npaper\, we demonstrate the use of the envelope method as a new parsimonious\nregression method for repeated measures analysis\, where the specification\nof the underlying pattern of time trend is not required by the model. We\nfound that if there is enough prior information to support the\nspecification of the functional dependency of the mean outcome on time and\nif the dimension of the prespecified functional form is low\, then the\nstandard method is advantageous as an efficient and unbiased estimator.\nOtherwise\, the envelope method is appealing as a more robust and\npotentially efficient parsimonious regression method in repeated measure\nanalysis. We compare the performance of the envelope estimators with the\nexisting estimators in simulation study and in an application to the China\nHealth and Nutrition Survey URL:https://css.pre2.ss.ucla.edu/event/lan-liu-university-of-minnesota-at-twin-cities/ CATEGORIES:CSS Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20190226T140000 DTEND;TZID=America/Los_Angeles:20190226T153000 DTSTAMP:20260610T204835 CREATED:20190228T181503Z LAST-MODIFIED:20190228T181503Z UID:1616-1551189600-1551195000@css.pre2.ss.ucla.edu SUMMARY:Adeline Lo\, Princeton University DESCRIPTION:Adeline Lo\, Princeton University \nAbstract: High dimensional (HD) data\, where the number of covariates and/or meaningful covariate interactions might exceed the number of observations\, is increasing used in prediction in the social sciences. An important question for the researcher is how to select the most predictive covariates among all the available covariates. Common covariate selection approaches use ad hoc rules to remove noise covariates\, or select covariates through the criterion of statistical significance or by using machine learning techniques. These can suffer from lack of objectivity\, choosing some but not all predictive covariates\, and failing reasonable standards of consistency that are expected to hold in most high-dimensional social science data. The literature is scarce in statistics that can be used to directly evaluate covariate predictivity. We address these issues by proposing a variable screening step prior to traditional statistical modeling\, in which we screen covariates for their predictivity. We propose the influence (I) statistic to evaluate covariates in the screening stage\, showing that the statistic is directly related to predictivity and can help screen out noisy covariates and discover meaningful covariate interactions. We illustrate how our screening approach can removing noisy phrases from U.S. Congressional speeches and rank important ones to measure partisanship. We also show improvements to out-of-sample forecasting in a state failure application. Our approach is applicable via an open-source software package. \nAdeline Lo is a postdoctoral research associate at the Department of Politics at Princeton University. Her research lies in the design of statistical tools for prediction and measurement for applied social sciences\, with a substantive interest in conflict and post-conflict processes. She has an ongoing research agenda on high dimensional forecasting\, especially in application to violent events. Her work has been published in the Proceedings of the National Academy of Sciences\, Comparative Political Studies and Nature. She will be joining the Department of Political Science at the University of Wisconsin-Madison as an Assistant Professor in Fall 2019. URL:https://css.pre2.ss.ucla.edu/event/adeline-lo-princeton-university-2/ CATEGORIES:CSS Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20190123T120000 DTEND;TZID=America/Los_Angeles:20190123T133000 DTSTAMP:20260610T204835 CREATED:20190119T195942Z LAST-MODIFIED:20190119T195942Z UID:1585-1548244800-1548250200@css.pre2.ss.ucla.edu SUMMARY:Kosuke Imai\, Harvard University DESCRIPTION:Title: Matching Methods for Causal Inference with Time-Series Cross-Section Data\nAbstract: Matching methods aim to improve the validity of causal inference in observational studies by reducing model dependence and offering intuitive diagnostics. While they have become a part of standard tool kit for empirical researchers across disciplines\, matching methods are rarely used when analyzing time-series cross-section (TSCS) data\, which consist of a relatively large number of repeated measurements on the same units. We develop a methodological framework that enables the application of matching methods to TSCS data. In the proposed approach\, we first match each treated observation with control observations from other units in the same time period that have an identical treatment history up to the pre-specified number of lags. We use standard matching and weighting methods to further refine this matched set so that the treated observation has outcome and covariate histories similar to those of its matched control observations. Assessing the quality of matches is done by examining covariate balance. After the refinement\, we estimate both short-term and long-term average treatment effects using the difference-in-differences estimator\, accounting for a time trend. We also show that the proposed matching estimator can be written as a weighted linear regression estimator with unit and time fixed effects\, providing model-based standard errors. We illustrate the proposed methodology by estimating the causal effects of democracy on economic growth\, as well as the impact of inter-state war on inheritance tax. The open-source software is available for implementing the proposed matching methods.\nCo-sponsored with the Political Science Department\, Statistics Department and the Center for Social Statistics\nMore on Prof. Imai URL:https://css.pre2.ss.ucla.edu/event/kosuke-imai-harvard-university-2/ LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States\, 5201 Sumner Ave\, United States\, 101 C St\, United States\, 301 C St\, United States CATEGORIES:CCPR Seminar,CSS Seminar,Divisional Publish ATTACH;FMTTYPE=: END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20190123T120000 DTEND;TZID=America/Los_Angeles:20190123T133000 DTSTAMP:20260610T204835 CREATED:20190118T194701Z LAST-MODIFIED:20190118T194701Z UID:1564-1548244800-1548250200@css.pre2.ss.ucla.edu SUMMARY:Kosuke Imai\, Harvard University DESCRIPTION:Title: Matching Methods for Causal Inference with Time-Series Cross-Section Data\nAbstract: Matching methods aim to improve the validity of causal inference in observational studies by reducing model dependence and offering intuitive diagnostics. While they have become a part of standard tool kit for empirical researchers across disciplines\, matching methods are rarely used when analyzing time-series cross-section (TSCS) data\, which consist of a relatively large number of repeated measurements on the same units. We develop a methodological framework that enables the application of matching methods to TSCS data. In the proposed approach\, we first match each treated observation with control observations from other units in the same time period that have an identical treatment history up to the pre-specified number of lags. We use standard matching and weighting methods to further refine this matched set so that the treated observation has outcome and covariate histories similar to those of its matched control observations. Assessing the quality of matches is done by examining covariate balance. After the refinement\, we estimate both short-term and long-term average treatment effects using the difference-in-differences estimator\, accounting for a time trend. We also show that the proposed matching estimator can be written as a weighted linear regression estimator with unit and time fixed effects\, providing model-based standard errors. We illustrate the proposed methodology by estimating the causal effects of democracy on economic growth\, as well as the impact of inter-state war on inheritance tax. The open-source software is available for implementing the proposed matching methods.\nCo-sponsored with the Political Science Department\, Statistics Department and the Center for Social Statistics\nMore on Prof. Imai URL:https://css.pre2.ss.ucla.edu/event/kosuke-imai-harvard-university/ LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States\, 101 Sumner Ave\, United States CATEGORIES:CCPR Seminar,CSS Seminar,Divisional Publish ATTACH;FMTTYPE=: END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20190117 DTEND;VALUE=DATE:20190118 DTSTAMP:20260610T204835 CREATED:20190117T212359Z LAST-MODIFIED:20190117T212359Z UID:1559-1547683200-1547769599@css.pre2.ss.ucla.edu SUMMARY:Test Divisional Event DESCRIPTION:CSS Event URL:https://css.pre2.ss.ucla.edu/event/test-divisional-event/ CATEGORIES:Divisional Publish END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20190116T120000 DTEND;TZID=America/Los_Angeles:20190116T133000 DTSTAMP:20260610T204835 CREATED:20190118T201223Z LAST-MODIFIED:20190118T201223Z UID:1570-1547640000-1547645400@css.pre2.ss.ucla.edu SUMMARY:Rocio Titiunik\, University of Michigan DESCRIPTION:Title: Internal vs. external validity in studies with incomplete populations \nAbstract: Researchers working with administrative data rarely have access to the entire universe of units they need to estimate effects and make statistical inferences. Examples are varied and come from different disciplines. In social program evaluation\, it is common to have data on all households who received the program\, but only partial information on the universe of households who applied or could have applied for the program. In studies of voter turnout\, information on the total number of citizens who voted is usually complete\, but data on the total number of voting-eligible citizens is unavailable at low levels of aggregation. In criminology\, information on arrests by race is available\, but the overall population that could have potentially been arrested is typically unavailable. And in studies of drug overdose deaths\, we lack complete information about the full population of drug users. \nIn all these cases\, a reasonable strategy is to study treatment effects and descriptive statistics using the information that is available. This strategy may lack the generality of a full-population study\, but may nonetheless yield valuable information for the included units if it has sufficient internal validity. However\, the distinction between internal and external validity is complex when the subpopulation of units for which information is available is not defined according to a reproducible criterion and/or when this subpopulation itself is defined by the treatment of interest. When this happens\, a useful approach is to consider the full range of conclusions that would be obtained under different possible scenarios regarding the missing information. I discuss a general strategy based on partial identification ideas that may be helpful to assess sensitivity of the partial-population study under weak (non-parametric) assumptions\, when information about the outcome variable is known with certainty for a subset of the units. I discuss extensions such as the inclusion of covariates in the estimation model and different strategies for statistical inference. \nCo-sponsored with the Political Science Department\, Statistics Department and the Center for Social Statistics \nMore on Prof. Titiunik URL:https://css.pre2.ss.ucla.edu/event/rocio-titiunik-university-of-michigan/ LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States CATEGORIES:CCPR Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20181017T120000 DTEND;TZID=America/Los_Angeles:20181017T133000 DTSTAMP:20260610T204835 CREATED:20181003T204102Z LAST-MODIFIED:20181003T204102Z UID:1525-1539777600-1539783000@css.pre2.ss.ucla.edu SUMMARY:Erin Hartman\, University of California Los Angeles DESCRIPTION:Title: Covariate Selection for Generalizing Experimental Results\nAbstract: Researchers are often interested in generalizing the average treatment effect (ATE) estimated in a randomized experiment to non-experimental target populations. Researchers can estimate the population ATE without bias if they adjust for a set of variables affecting both selection into the experiment and treatment heterogeneity. Although this separating set has simple mathematical representation\, it is often unclear how to select this set in applied contexts. In this paper\, we propose a data-driven method to estimate a separating set. Our approach has two advantages. First\, our algorithm relies only on the experimental data. As long as researchers can collect a rich set of covariates on experimental samples\, the proposed method can inform which variables they should adjust for. Second\, we can incorporate researcher-specific data constraints. When researchers know certain variables are unmeasurable in the target population\, our method can select a separating set subject to such constraints\, if one is feasible. We validate our proposed method using simulations\, including naturalistic simulations based on real-world data.\nCo-Sponsored with The Center for Social Statistics\nMore on Prof. Hartman URL:https://css.pre2.ss.ucla.edu/event/erin-hartman-university-of-california-los-angeles/ LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States\, 101 Sumner Ave\, United States CATEGORIES:CCPR Seminar,CSS Seminar,Divisional Publish ORGANIZER;CN="CCPR%20Seminars":MAILTO:seminars@ccpr.ucla.edu END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20180412T120000 DTEND;TZID=America/Los_Angeles:20180412T130000 DTSTAMP:20260610T204835 CREATED:20180403T170740Z LAST-MODIFIED:20180403T170740Z UID:1493-1523534400-1523538000@css.pre2.ss.ucla.edu SUMMARY:Workshop: Bayesian Concepts for Data Analysis DESCRIPTION:Instructor: Michael Tzen \nContent:\nThis 1 hour workshop will provide a sampling of introductory concepts for bayesian analysis. We will use Bayes Rule (and its implications) to think about data analysis. When used as a framework to model phenomenon\, the analyst gets to work with 4 useful distributions: the prior\, posterior\, prior predictive\, & posterior predictive. We will predict what clothing size 2Chainz wears. We’ll also look at the Gompertz Rule from demography. In both examples\, the bayesian framework allows us to clearly express the estimand\, information from data\, information from prior knowledge\, and the estimator. \nThis workshop is the first of a two part series. The first workshop is conceptual while the second workshop will focus on software. The date for the second workshop is TBD. \nPlease RSVP Here: \nhttps://goo.gl/forms/CF4wuaobfqpug9Js1 \n  URL:https://css.pre2.ss.ucla.edu/event/workshop-bayesian-concepts-for-data-analysis/ LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States CATEGORIES:CSS Workshop END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20180313T140000 DTEND;TZID=America/Los_Angeles:20180313T151500 DTSTAMP:20260610T204835 CREATED:20180312T165958Z LAST-MODIFIED:20180312T165958Z UID:1486-1520949600-1520954100@css.pre2.ss.ucla.edu SUMMARY:Jake Bowers\, University of Illinois at Urbana-Champaign DESCRIPTION:The UCLA Department of Statistics and the Center for Social Statistics presents:\nRules of Engagement in Evidence-Informed Policy: Practices and Norms of Statistical Science in Government\n\nCollaboration between statistical scientists (data scientists\, behavioral and social scientists\, statisticians) and policy makers promises to improve government and the lives of the public. And the data and design challenges arising from governments offer academics new chances to improve our understanding of both extant methods and behavioral and social science theory. However\, the practices that ensure the integrity of statistical work in the academy — such as transparent sharing of data and code — do not translate neatly or directly into work with governmental data and for policy ends. This paper proposes a set of practices and norms that academics and practitioners can agree on before launching a partnership so that science can advance and the public can be protected while policy can be improved. This work is at an early stage. The aim is a checklist or statement of principles or memo of understanding that can be a template for the wide variety of ways that statistical scientists collaborate with governmental actors. \n\nSpeaker:\nJake Bowers\, Associate Professor at University of Illinois and Fellow of the Office of Evaluation Sciences\nsite URL:https://css.pre2.ss.ucla.edu/event/jake-bowers-university-of-illinois-at-urbana-champaign/ LOCATION:Franz Hall 2258A\, Franz Hall 2258A CATEGORIES:CSS Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20180221T120000 DTEND;TZID=America/Los_Angeles:20180221T133000 DTSTAMP:20260610T204835 CREATED:20180209T002136Z LAST-MODIFIED:20180209T002136Z UID:1413-1519214400-1519219800@css.pre2.ss.ucla.edu SUMMARY:Yu Xie\, Princeton DESCRIPTION:The California Center for Population Research and the Center for Social Statistics Presents:\nHeterogeneous Causal Effects: A Propensity Score Approach\nHeterogeneity is ubiquitous in social science.  Individuals differ not only in background characteristics\, but also in how they respond to a particular treatment. In this presentation\, Yu Xie argues that a useful approach to studying heterogeneous causal effects is through the use of the propensity score. He demonstrates the use of the propensity score approach in three scenarios: when ignorability is true\, when treatment is randomly assigned\, and when ignorability is not true but there are valid instrumental variables. \nSpeaker:\nYu Xie\, Professor\, Princeton\nsite URL:https://css.pre2.ss.ucla.edu/event/yu-xie-princeton/ LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States CATEGORIES:CSS Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20180206T140000 DTEND;TZID=America/Los_Angeles:20180206T153000 DTSTAMP:20260610T204835 CREATED:20180129T183306Z LAST-MODIFIED:20180129T183306Z UID:1368-1517925600-1517931000@css.pre2.ss.ucla.edu SUMMARY:Per Block\, ETH Zurich (Swiss Federal Institute of Technology in Zurich) DESCRIPTION:The UCLA Department of Statistics and the Center for Social Statistics presents:\nModelling Mobility Tables as Weighted Networks\nContemporary research on occupational mobility\, i.e. how people move between jobs\, tends to view mobility as being mostly determined by individual and occupational characteristics. These studies focus on people’s sex\, ethnicity\, age\, education or class origin and how they get access to jobs of different wages\, working conditions\, desirability\, skill profiles and job security. Consequently\, observations in occupational mobility tables are understood as independent of one another\, which allows the use of a variety of well-developed statistical models. As opposed to these “classical” approaches focussed on individual and occupational characteristics\, I am interested in modelling and understanding endogenously emerging patterns in occupational mobility tables. These emergent patterns arise from the social embedding of occupational choices\, when occupational transitions of different individuals influence each other. To analyse these emergent patterns\, I conceptualise a disaggregated mobility table as a network in which occupations are the nodes and connections are made of individuals transitioning between occupations.\n\n\nIn this paper\, I present a statistical model to analyse these weighted mobility networks. The approach to modelling mobility as an interdependent system is inspired by the exponential random graph model (ERGM); however\, some differences arise from ties being weighted as well as from specific constraints of mobility tables. The model is applied to data on intra-generational mobility to analyse the interdependent transitions of men and women through the labour market\, as well as to understanding the extent to which clustering in mobility can be modelled by exogenously defined social classes or through endogenous structures.\n  \nPer Block\, ETH Zurich (Swiss Federal Institute of Technology in Zurich)\nsite URL:https://css.pre2.ss.ucla.edu/event/per-block-eth-zurich/ LOCATION:Franz Hall 2258A\, Franz Hall 2258A CATEGORIES:CSS Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20180124T120000 DTEND;TZID=America/Los_Angeles:20180124T133000 DTSTAMP:20260610T204835 CREATED:20180119T224032Z LAST-MODIFIED:20180119T224032Z UID:1361-1516795200-1516800600@css.pre2.ss.ucla.edu SUMMARY:Rob Warren\, University of Minnesota DESCRIPTION:The California Center for Population Research and the Center for Social Statistics presents:\nWhen Should Researchers Use Inferential Statistics When Analyzing Data on Full Populations?\nMany researchers uncritically use inferential statistical procedures (e.g.\, hypothesis tests) when analyzing complete population data—a situation in which inference may seem unnecessary. We begin by reviewing and analyzing the most common rationales for employing inferential procedures when analyzing full population data. Two common rationales—having to do with handling missing data and generalizing results to other times and/or places—either lack merit or amount to analyzing sample (not population) data.  Whether it is appropriate to use inferential procedures depends on whether researchers are analyzing sample or population data and on whether they seek to make causal or descriptive claims. When doing descriptive research\, the distinction between sample and population data is paramount: Inferential statistics should only be used to analyze sample data (to account for sampling variability) and never to analyze population data. When doing causal research\, the distinction between sample data and population data is unimportant: Inferential procedures can and should always be used to distinguish (for example) robust associations from those that may have come about by chance alone. Crucially\, using inferential procedures to analyze population data to make descriptive claims can lead to incorrect substantive conclusions—especially when population sizes and/or effect sizes are small. \nSpeaker:\nRob Warren\, Professor of Sociology and Director of the Minnesota Population Center\nsite URL:https://css.pre2.ss.ucla.edu/event/rob-warren-university-minnesota/ LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States CATEGORIES:CSS Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20171212T140000 DTEND;TZID=America/Los_Angeles:20171212T153000 DTSTAMP:20260610T204835 CREATED:20171211T190142Z LAST-MODIFIED:20171211T190142Z UID:1354-1513087200-1513092600@css.pre2.ss.ucla.edu SUMMARY:Nathaniel Osgood\, University of Saskatchewan DESCRIPTION:The UCLA Department of Community Health Sciences and the Center for Social Statistics presents:\nDynamic Modeling for Health in the Age of Big Data\nTraditional approaches to public health concerns have conferred great advances in the duration and quality of life. Public health interventions – from improved sanitation efforts\, to vaccination campaigns\, to contact tracing and environmental regulations – have helped reduce common risks to health throughout many areas of the world. Unfortunately\, while traditional methods from the health sciences have proven admirably suited for addressing traditional challenges\, a troubling crop of complex health challenges confront the nation and the world\, and threaten to stop – and even reverse the – rise in length and quality of life that many have taken for granted. Examples include multi-factorial problems such as obesity and obesity-related chronic disease\, the spread of drug-resistant and rapidly mutating pathogens that evade control efforts\, and “syndemics” of mutually reinforcing health conditions (such as Diabetes and TB; substance abuse\, violence and HIV/AIDS; obesity & stress). Such challenges have proven troublingly policy resistant\, with interventions being thwarted by “blowback” from the complex feedbacks involved\, and attendant costs threaten to overwhelm health care systems. In the face of such challenges public health decision makers are increasingly supplementing their toolbox using “system science” techniques. Such methods – also widely known as “complex systems approaches” – provide a way to understand a system’s behavior as a whole and as more than the sum of its parts\, and a means of anticipating and managing the behavior of a system in more judicious and proactive fashion. However\, such approaches offer substantially greater insight and power when combined with rich data sources. Within this talk\, we will highlight the great promise afforded by combining of Systems Science techniques and rich data sources\, particularly emphasizing the role of cross-linking models with “big data” offering high volume\, velocity\, variety and veracity. Examples of such data include fine-grained temporal and spatial information collected by smartphone-based and wearable as well as building and municipal sensors\, data from social media posts and search behavior\, helpline calls\, website accesses and rich cross-linked databases. Decision-oriented models grounded by such novel data sources can allow for articulated theory building regarding difficult-to-observe aspects of human behavior. Such models can also aid in informing evaluation of and judicious selection between sophisticated interventions to lessen the health burden of a wide variety of health conditions. Such models are particularly powerful when complemented by machine learning and computational statistics techniques that permit recurrent model regrounding in the newest evidence\, and which allow a model to knit together holistic portrait of the system as a whole\, and which support grounded investigation of between intervention strategies tradeoffs. \nSponsored by The Department of Community Health Sciences along with the Center for Social Statistics and the California Center for Population Research \nSpeaker:\nNathaniel Osgood\, Professor\, Department of Computer Science\, Associate Faculty at Department of Community Health & Epidemiology and Bioengineering Division at the University of Saskatchewan URL:https://css.pre2.ss.ucla.edu/event/nathaniel-osgood-saskatchewan/ LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States CATEGORIES:CSS Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20171108T153000 DTEND;TZID=America/Los_Angeles:20171108T163000 DTSTAMP:20260610T204835 CREATED:20171103T183129Z LAST-MODIFIED:20171103T183129Z UID:1334-1510155000-1510158600@css.pre2.ss.ucla.edu SUMMARY:Hadley Wickham\, RStudio DESCRIPTION:The UCLA Department of Statistics and the Center for Social Statistics presents:\nProgramming data science with R & the tidyverse\nTidy evaluation is a new framework for non-standard evaluation that\nwill be used throughout tidyverse. In this talk\, I’ll introduce you to\nthe problem that tidy eval solves\, illustrated with examples of the\nvarious approaches used in R. I’ll then explain the most important\ncomponents so that you can start writing your own functions instead of\ncopying and pasting tidyr and dplyr code. I’ll finish with a small\nshiny app that shows how tidy eval is a natural fit for handling user\ninput. \nHadley Wickham\, RStudio\nhttp://hadley.nz/ URL:https://css.pre2.ss.ucla.edu/event/hadley-wickham-rstudio/ LOCATION:1200 Rolfe Hall\, 1200 Rolfe Hall CATEGORIES:CSS Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20171024T140000 DTEND;TZID=America/Los_Angeles:20171024T150000 DTSTAMP:20260610T204835 CREATED:20171016T182654Z LAST-MODIFIED:20171016T182654Z UID:1324-1508853600-1508857200@css.pre2.ss.ucla.edu SUMMARY:Sander Greenland\, UCLA Department of Epidemiology DESCRIPTION:The UCLA Department of Statistics and the Center for Social Statistics presents:\nStatistical Significance and Discussion of the Challenges of Avoiding the Abuse of Statistical Methodology\nSander Greenland will offer his perspective on the paper\, “Redefine Statistical Significance”\, which was the topic of the previous week’s seminar. Also he will discuss the challenges of avoiding the abuse of statistical methodology. \nSpeaker:\nSander Greenland\, Professor Emeritus\, UCLA Department of Epidemiology URL:https://css.pre2.ss.ucla.edu/event/sander-greenland-ucla/ LOCATION:1434A Physics and Astronomy\, 1434A Physics and Astronomy\, Los Angeles\, CA\, 90098\, United States CATEGORIES:CSS Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20171017T140000 DTEND;TZID=America/Los_Angeles:20171017T150000 DTSTAMP:20260610T204835 CREATED:20210424T023825Z LAST-MODIFIED:20210424T023825Z UID:2081-1508248800-1508252400@css.pre2.ss.ucla.edu SUMMARY:Daniel Benjamin\, USC Dornsife Center for Economic and Social Research DESCRIPTION:The UCLA Department of Statistics and the Center for Social Statistics presents:\nRedefine Statistical Significance\nDaniel Benjamin will discuss his paper (written by him and 71 other authors)\, “Redefine Statistical Significance”. The paper proposes that the default p-value threshold should be changed from 0.05 to 0.005. \nThe paper is available at this link. \nSpeaker:\nDaniel Benjamin\, Associate Professor\, USC Dornsife Center for Economic and Social Research \n  URL:https://css.pre2.ss.ucla.edu/event/daniel-benjamin-usc-dornsife-center-for-economic-and-social-research/ CATEGORIES:CSS Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20171017T140000 DTEND;TZID=America/Los_Angeles:20171017T150000 DTSTAMP:20260610T204835 CREATED:20171016T181816Z LAST-MODIFIED:20171016T181816Z UID:1315-1508248800-1508252400@css.pre2.ss.ucla.edu SUMMARY:Daniel Benjamin\, USC Dornsife Center for Economic and Social Research DESCRIPTION:The UCLA Department of Statistics and the Center for Social Statistics presents:\nRedefine Statistical Significance\nDaniel Benjamin will discuss his paper (written by him and 71 other authors)\, “Redefine Statistical Significance”. The paper proposes that the default p-value threshold should be changed from 0.05 to 0.005. \nThe paper is available at this link. \nSpeaker:\nDaniel Benjamin\, Associate Professor\, USC Dornsife Center for Economic and Social Research \n  URL:https://css.pre2.ss.ucla.edu/event/daniel-benjamin-usc/ LOCATION:1434A Physics and Astronomy\, 1434A Physics and Astronomy\, Los Angeles\, CA\, 90098\, United States CATEGORIES:CSS Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20171013T120000 DTEND;TZID=America/Los_Angeles:20171013T130000 DTSTAMP:20260610T204835 CREATED:20170928T001141Z LAST-MODIFIED:20170928T001141Z UID:1307-1507896000-1507899600@css.pre2.ss.ucla.edu SUMMARY:Workshop: Useful R 4 Stata Users Brown Bag DESCRIPTION:Instructor: \nMichael Tzen\, CCPR UCLA \n  \nAbstract: \n\n\n\nThis workshop is a brown bag forum. Participants are encouraged to bring in tangible questions they wish to explore using R. To serve as a background road map\, the instructor will provide an abbreviated sample of what he thinks are the most useful features of R. However\, the goal is to have participants ask questions that the collective group can figure out using R. Any R question is fair game\, for example: questions about fundamental R concepts or even questions about how to run Stata-equivalent R commands. Participants will be provided access to Rstudio\, so please bring a laptop. \nThis CCPR brown-bag is intended to be an open forum that complements the 3 great resources below. Please see the resources\, especially the first one. \n1) 10 minute demo: interactive call–response slideshow of R basics\nhttp://tryr.codeschool.com/ \n2) Worked out examples from a UCLA IDRE workshop on R concepts\nhttps://stats.idre.ucla.edu/r/seminars/intro/ \n3) R 4 Data Science e-book\nhttp://r4ds.had.co.nz/ \n\n\n\n\n  \n  \nRSVP Signup: \n\nplease fill out the form found here URL:https://css.pre2.ss.ucla.edu/event/workshop-useful-r-4-stata-users-brown-bag/ CATEGORIES:CCPR Workshop,CSS Workshop END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20170609T120000 DTEND;TZID=America/Los_Angeles:20170609T130000 DTSTAMP:20260610T204835 CREATED:20170530T165922Z LAST-MODIFIED:20170530T165922Z UID:1262-1497009600-1497013200@css.pre2.ss.ucla.edu SUMMARY:James Robins\, Harvard University DESCRIPTION:The UCLA Departments of Epidemiology\, Biostatistics\, Statistics and the Center for Social Statistics presents:\nCausal Methods in Epidemiology: Where has it got us and what can we expect in the future?\nThe principal focus of Dr. Robins’ research has been the development of analytic methods appropriate for drawing causal inferences from complex observational and randomized studies with time-varying exposures or treatments. The new methods are to a large extent based on the estimation of the parameters of a new class of causal models – the structural nested models – using a new class of estimators – the G estimators.\n\nPlease RSVP: https://goo.gl/wScewQ \n\nSpeaker:\nJames Robins\, Mitchell L. and Robin LaFoley Dong Professor of Epidemiology\, Harvard University\nhttps://www.hsph.harvard.edu/james-robins/ URL:https://css.pre2.ss.ucla.edu/event/james-robins-harvard-university/ LOCATION:Room 33-105 CHS Building\, 650 Charles E Young Drive South\, Los Angeles\, CA\, 90095 \, United States CATEGORIES:CSS Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20170602T120000 DTEND;TZID=America/Los_Angeles:20170602T160000 DTSTAMP:20260610T204835 CREATED:20170512T203737Z LAST-MODIFIED:20170512T203737Z UID:1235-1496404800-1496419200@css.pre2.ss.ucla.edu SUMMARY:Fragile Families Challenge: Getting Started Workshop DESCRIPTION:Instructor: \nIan Lundberg\, Ph.D. Student\, Sociology and Social Policy\, Princeton University \n  \nAbstract: \nThe Fragile Families Challenge is a scientific mass collaboration that combines predictive modeling\, causal inference\, and in-depth interviews in order to learn more about the lives of disadvantaged children. Fragile Families Challenge builds on the Fragile Families and Child Wellbeing Study that has been running for about 20 years. The Fragile Families research team has been following about 5\,000 families—collecting information about them and their environment at regular intervals—in order to understand how to improve the lives of disadvantaged children in the US. \n*Co-Sponsored with the California Center for Population Research\, UCLA \nRegister now @ fragilefamilieschallenge.org\, please specify you plan to attend the UCLA workshop URL:https://css.pre2.ss.ucla.edu/event/fragile-families-challenge-getting-started-workshop/ LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States CATEGORIES:CCPR Workshop,CSS Workshop END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20170524T120000 DTEND;TZID=America/Los_Angeles:20170524T133000 DTSTAMP:20260610T204835 CREATED:20170323T201513Z LAST-MODIFIED:20170323T201513Z UID:784-1495627200-1495632600@css.pre2.ss.ucla.edu SUMMARY:Shahryar Minhas\, Duke University DESCRIPTION:The Center for Social Statistics Presents:\nPredicting the Evolution of Intrastate Conflict: Evidence from Nigeria\nThe endogenous nature of civil conflict has limited scholars’ abilities to draw clear inferences about the drivers of conflict evolution. We argue that three primary features characterize the complexity of intrastate conflict: (1) the interdependent relationships of conflict between actors; (2) the impact of armed groups on violence as they enter or exit the conflict network; and (3) the ability of civilians to influence the strategic interactions of armed groups. Using ACLED event data on Nigeria\, we apply a novel network-based approach to predict the evolution of intrastate conflict dynamics. Our network approach yields insights about the effects of civilian victimization and key actors entering the conflict. Attacks against civilians lead groups to both be more violent\, and to become the targets of attacks in subsequent periods. Boko Haram’s entrance into the civil war leads to an increase in violence even in unrelated dyads. Further\, our approach significantly outperforms more traditional dyad-group approaches at predicting the incidence of conflict.\n  \nSpeaker: \nShahryar Minha\, Postdoctoral Fellow\, Duke University\nAssistant Professor\, Michigan State University\nDepartment of Political Science and the Social Science Data Analytics Program (SSDA) \nhttp://s7minhas.com/ URL:https://css.pre2.ss.ucla.edu/event/shahryar-minhas/ LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States CATEGORIES:CSS Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20170519T120000 DTEND;TZID=America/Los_Angeles:20170519T133000 DTSTAMP:20260610T204835 CREATED:20170502T232838Z LAST-MODIFIED:20170502T232838Z UID:1227-1495195200-1495200600@css.pre2.ss.ucla.edu SUMMARY:Analysis of Complex Surveys using R and Stata DESCRIPTION:Instructors: \nMichael Tzen\, CCPR UCLA \nAndy Lin\, IDRE UCLA \n  \nAbstract: \nIn this workshop\, attendees will learn how to analyze survey data while accounting for its complex survey design. Using both the R and Stata software packages\, we will demonstrate how to specify the survey design\, impute any missing data\, and analyze the survey outcomes of interest. We will discuss how our downstream “analysis” steps are related to initial operational “design” choices made by the survey data provider. \n  \nNote: \nPlease bring your own laptop.\nFor R\, we will use instantaneous Jupyter notebooks (no install required)\nFor Stata\, please have a TS1 account or your own copy of Stata\n(for software assistance\, contact emoss@ccpr.ucla.edu)\nRSVP Signup: \nplease fill out the form found here URL:https://css.pre2.ss.ucla.edu/event/1227/ LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States CATEGORIES:CCPR Workshop,CSS Workshop END:VEVENT END:VCALENDAR